Divide and conquer approach for genome-wide association studies.

ABSTRACT: Genome-wide association studies (GWAS) are computationally intensive, requiring significant time and resources with computational complexity scaling at least linearly with sample size. Here, we present an accurate and resource-efficient pipeline for GWAS that mitigates the impact of sample size on computational demands. Our approach involves (1) randomly partitioning the cohort into equally sized sub-cohorts, (2) conducting independent GWAS within each sub-cohort, and (3) integrating the results using a novel meta-analysis technique that accounts for population structure and other confounders between sub-cohorts. Importantly, we demonstrate through simulations and real-data examples in humans that our approach effectively manages analyzing related individuals, a critical factor in real dat

SUBMITTER: Ozkaraca MI 

PROVIDER: S-EPMC12005250 | biostudies-literature | 2025 Apr

REPOSITORIES: biostudies-literature